Using Deep Learning to Predict Treatment Response in Patients with Hepatocellular Carcinoma Treated with Y90 Radiation Segmentectomy.
Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose–response threshold, where dose estimates are highly dependent on accurate SPECT/CT acquisition, registration, and reconstruction. Any error can result in distorted absorbed dose di...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 3; pp. 1180 - 1189 |
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| Autores principales: | , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164473091&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164473091 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 164473091 161251640 164473091 164473091 10.1007/s10278-022-00762-0 164473091 ppf: 1180 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Deep Learning to Predict Treatment Response in Patients with Hepatocellular Carcinoma Treated with Y90 Radiation Segmentectomy. aug: au: Wagstaff, William V. Villalobos, Alexander Gichoya, Judy Kokabi, Nima affil: Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA sug: subj: Carcinoma, Hepatocellular Diagnosis Carcinoma, Hepatocellular Radiography Deep Learning Treatment Outcomes Radiography, Interventional Dosimetry Embolization, Therapeutic Liver Neoplasms Radiotherapy Radioembolization Lumpectomy Dose-Response Relationship Retrospective Design Magnetic Resonance Imaging Algorithms Machine Learning Pneumonectomy Radioisotopes Data Analysis Software Descriptive Statistics Human Male Female Adult Middle Age Aged Radioisotopes Therapeutic Use Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose–response threshold, where dose estimates are highly dependent on accurate SPECT/CT acquisition, registration, and reconstruction. Any error can result in distorted absorbed dose distributions and inaccurate estimates of treatment success. This study improves upon the voxel-based dosimetry model, one of the most accurate methods available clinically, by using a deep convolutional network ensemble to account for the spatially variable uptake of Y90 within a treated lesion. A retrospective analysis was conducted in patients with HCC who received Y90-RE at a single institution. Seventy-seven patients with 103 lesions met the inclusion criteria: three or fewer tumors, pre- and post treatment MRI, and no prior Y90-RE. Lesions were labeled as complete (n = 57) or incomplete response (n = 46) based on 3-month post treatment MRI and divided by medical record number into a 20% hold-out test set and 80% training set with 5-fold cross-validation. Slice-wise predictions were made from an average ensemble of models and thresholds from the highest accuracy epochs across all five folds. Lesion predictions were made by thresholding all slice predictions through the lesion. When compared to the voxel-based dosimetry model, our model had a higher F1-score (0.72 vs. 0.2), higher accuracy (0.65 vs. 0.60), and higher sensitivity (1.0 vs. 0.11) at predicting complete treatment response. This algorithm has the potential to identify patients with treatment failure who may benefit from earlier follow-up or additional treatment. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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